differential_expression_analysis
Mohit Rastogi
September 27, 2022
Last updated: 2022-09-27
Checks: 7 0
Knit directory: HumanMultiomics/
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Introduction
This is the differential expression analysis from the total RNA sequencing experiment performed on postmortem hippocampus obtained from Down syndrome and control individuals.
First, we will load all the packages required to perform this analysis
library(ggplot2)
library(dplyr)
library(edgeR)
library(openxlsx)
library(ggrepel)
library(DBI)
library(org.Hs.eg.db)
library("pcaExplorer")
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(rtracklayer)
library(ggbio)
library(clusterProfiler)
library(enrichplot)
library(ggridges)
library(karyoploteR)
library(tidyverse)
library(forcats)
library(pathview)
library(ComplexHeatmap)
library(circlize)
library(kableExtra)
library(DT)Read the raw count matrix for hippocampus samples
The count matrix consists of 11 control samples (C1-C11) and 9 Down syndrome samples (Ds1-Ds9)
count_human_total_hippocampus_coding<- read.table(file= "data/raw_counts_human_hippo_matrix_coding.txt", header=T, row.names=1)
head(count_human_total_hippocampus_coding) C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 Ds1 Ds2 Ds3
ENSG00000186092 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284733 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284662 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000187634 9 9 36 40 8 11 12 69 6 28 12 26 40 29
ENSG00000188976 496 624 1482 1417 558 454 1085 1249 270 477 799 1313 351 629
ENSG00000187961 13 20 89 66 12 34 82 131 13 30 25 105 44 45
Ds4 Ds5 Ds6 Ds7 Ds8 Ds9
ENSG00000186092 0 0 0 0 0 0
ENSG00000284733 0 0 0 0 0 0
ENSG00000284662 0 0 0 0 0 0
ENSG00000187634 46 47 21 41 26 22
ENSG00000188976 1051 798 755 570 887 1260
ENSG00000187961 95 98 82 47 32 100meta_counts_human_total_hippocampus_coding<-data.frame(row.names = colnames(count_human_total_hippocampus_coding),condition_human_total_hippocampus_coding=c("Cont","Cont","Cont","Cont","Cont","Cont","Cont","Cont","Cont","Cont","Cont","DS","DS","DS","DS","DS","DS","DS","DS","DS"))Define the groups
group_human_total_hippocampus_coding<-relevel(factor(meta_counts_human_total_hippocampus_coding$condition_human_total_hippocampus_coding),ref="Cont")
group_human_total_hippocampus_coding [1] Cont Cont Cont Cont Cont Cont Cont Cont Cont Cont Cont DS DS DS DS
[16] DS DS DS DS DS
Levels: Cont DSDesign the model for performing differential expression analysis
#Design the model for the differential expression
design_human_total_hippocampus_coding = model.matrix(~group_human_total_hippocampus_coding)
y_human_total_hippocampus_coding<-DGEList(count_human_total_hippocampus_coding,group = group_human_total_hippocampus_coding)Calculate counts per million in log
cpm_log_human_total_hippocampus_coding<-cpm(y_human_total_hippocampus_coding,log=TRUE)Visualize the log counts per million (cpm) as a heatmap
heatmap(cor(cpm_log_human_total_hippocampus_coding))
Perform PCA after loading the sample information
sample_hippo<- read.xlsx("data/human_hippo_samplesheet.xlsx", sheet=1)
row.names(sample_hippo)<-sample_hippo$SampleName
all(colnames(cpm_log_human_total_hippocampus_coding) == rownames(sample_hippo))[1] TRUEp <- PCAtools::pca(cpm_log_human_total_hippocampus_coding, metadata = sample_hippo, removeVar = 0.1)-- removing the lower 10% of variables based on variancePCAtools::screeplot(p)
PCAtools::biplot(p)
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
Filter the data based on heatmap, PCA and RIN values
Looking at the clustering and PCA plots, we remove few samples which look like outliers: C6,C7,C8,C9,C10,Ds2,Ds6,Ds7
count_hth_filt_coding2<-count_human_total_hippocampus_coding[,-c(6,7,8,9,10,13,17,18)]
head(count_hth_filt_coding2) C1 C2 C3 C4 C5 C11 Ds1 Ds3 Ds4 Ds5 Ds8 Ds9
ENSG00000186092 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284733 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284662 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000187634 9 9 36 40 8 12 26 29 46 47 26 22
ENSG00000188976 496 624 1482 1417 558 799 1313 629 1051 798 887 1260
ENSG00000187961 13 20 89 66 12 25 105 45 95 98 32 100meta_counts_hth_filt_coding2<-data.frame(row.names = colnames(count_hth_filt_coding2),condition_hth_filt_coding2=c("Cont","Cont","Cont","Cont","Cont","Cont","DS","DS","DS","DS","DS","DS"))####Perform hierarchical clsutering and PCA on the filtered samples
group_hth_filt_coding2<-relevel(factor(meta_counts_hth_filt_coding2$condition_hth_filt_coding2),ref="Cont")
group_hth_filt_coding2 [1] Cont Cont Cont Cont Cont Cont DS DS DS DS DS DS
Levels: Cont DSdesign_hth_filt_coding2 = model.matrix(~group_hth_filt_coding2)
y_hth_filt_coding2<-DGEList(count_hth_filt_coding2,group = group_hth_filt_coding2)
cpm_log_hth_filt_coding2<-cpm(y_hth_filt_coding2,log=TRUE)
heatmap(cor(cpm_log_hth_filt_coding2))
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
sample_hippo<- read.xlsx("data/human_hippo_samplesheet.xlsx", sheet=2)
row.names(sample_hippo)<-sample_hippo$SampleName
all(colnames(cpm_log_hth_filt_coding2) == rownames(sample_hippo))[1] TRUEp <- PCAtools::pca(cpm_log_hth_filt_coding2, metadata = sample_hippo, removeVar = 0.1)-- removing the lower 10% of variables based on variancePCAtools::screeplot(p)
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
PCAtools::biplot(p)
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
Filter the genes based on their expression in n-1 samples
cpm_y_hth_filt_coding2<-cpm(y_hth_filt_coding2)
keep_hth_filt_coding2<-rowSums((cpm_y_hth_filt_coding2)>1)>5
table(keep_hth_filt_coding2)keep_hth_filt_coding2
FALSE TRUE
5747 14221 Perform normalization using TMM
y_hth_filt_coding2<-DGEList(count_hth_filt_coding2,group = group_hth_filt_coding2)
y_hth_filt_coding2 <- y_hth_filt_coding2[keep_hth_filt_coding2, , keep.lib.sizes=FALSE]
y_hth_filt_coding2 <- calcNormFactors(y_hth_filt_coding2)
y_hth_filt_coding2$samples group lib.size norm.factors
C1 Cont 26747264 0.9014953
C2 Cont 28753065 0.9295325
C3 Cont 31216269 1.0367848
C4 Cont 34181183 0.9965316
C5 Cont 24231673 0.9048995
C11 Cont 23897314 0.9656747
Ds1 DS 27072147 1.0308510
Ds3 DS 17654033 1.1121233
Ds4 DS 27713628 1.0497314
Ds5 DS 25307466 1.1031263
Ds8 DS 26297451 0.9796220
Ds9 DS 24491826 1.0163664Estimate differential expression using GLM
y_hth_filt_coding2 <- estimateGLMRobustDisp(y_hth_filt_coding2,design_hth_filt_coding2, verbose = TRUE)Iteration 1: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 2: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 3: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 4: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 5: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 6: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.fit_hth_filt_coding2 <- glmFit(y_hth_filt_coding2, design_hth_filt_coding2)
lrt_hth_filt_coding2<-glmLRT(fit_hth_filt_coding2,coef = 2)
de_hth_filt_coding2 <- decideTestsDGE(lrt_hth_filt_coding2, adjust.method="BH", p.value = 0.05)Number of differentially expressed genes in the hippocampus
summary(de_hth_filt_coding2) group_hth_filt_coding2DS
Down 2205
NotSig 9394
Up 2622Counts per million (CPM) values for all the genes considered in the differential expression analysis
cpm_y_hth_filt_coding22<-cpm(y_hth_filt_coding2)
cpm_y_hth_filt.dt <- DT::datatable(cpm_y_hth_filt_coding22, rownames=TRUE, class="white-space: nowrap", escape=FALSE)
cpm_y_hth_filt.dtHere, we annotated the data from the previous using ENSEMBL information for each gene and mapped the DEGs onto their chromosomal location.
OrgDb <- org.Hs.eg.db
results_edgeR_hth_filt_coding2<- topTags(lrt_hth_filt_coding2, n = nrow(count_hth_filt_coding2), sort.by = "none")
k<-row.names(results_edgeR_hth_filt_coding2)
ann_hth_filt_coding2<-AnnotationDbi::select(org.Hs.eg.db,keys=k,keytype = "ENSEMBL",columns=c("ENTREZID","SYMBOL","GENENAME","CHR","UNIPROT","ALIAS","GENENAME"))'select()' returned 1:many mapping between keys and columnsidx<-match(row.names(results_edgeR_hth_filt_coding2),ann_hth_filt_coding2$ENSEMBL)
results_rna_annotated_hth_filt_coding2<-cbind(results_edgeR_hth_filt_coding2,ann_hth_filt_coding2[idx,])
detags <- rownames(y_hth_filt_coding2)[as.logical(de_hth_filt_coding2)]
sigGenes <- results_rna_annotated_hth_filt_coding2[detags,]
tx<-TxDb.Hsapiens.UCSC.hg38.knownGene
exo <- exonsBy(tx,"gene")
exoRanges <- unlist(range(exo))
sigRegions <- exoRanges[na.omit(match(sigGenes$ENTREZID, names(exoRanges)))]
mcols(sigRegions) <- sigGenes[match(names(sigRegions), sigGenes$ENTREZID),]
sigRegions[order(sigRegions$LR,decreasing = TRUE)]GRanges object with 4809 ranges and 12 metadata columns:
seqnames ranges strand | logFC logCPM LR
<Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric>
64005 chr7 44962662-44979088 - | 3.67879 0.606683 66.0467
7052 chr20 38127385-38166578 - | 2.25840 4.274056 65.9522
3339 chr1 21822244-21937310 - | 2.44714 3.740847 65.5580
12 chr14 94592058-94624646 + | 3.16526 0.988412 64.9642
3691 chr17 75721328-75757818 + | 2.18007 5.528861 64.2538
... ... ... ... . ... ... ...
10745 chr1 113696831-113759489 - | -0.361545 5.48447 5.71151
8495 chr11 7513298-7657127 + | 0.496606 4.93888 5.71125
54039 chr21 45643694-45942454 + | 0.639404 3.11438 5.70519
57592 chr1 151281618-151292176 + | 0.795094 3.55223 5.70267
8708 chr2 167293171-167558333 + | -0.444137 5.26416 5.70133
PValue FDR ENSEMBL ENTREZID SYMBOL
<numeric> <numeric> <character> <character> <character>
64005 4.40354e-16 2.67493e-12 ENSG00000136286 64005 MYO1G
7052 4.61983e-16 2.67493e-12 ENSG00000198959 7052 TGM2
3339 5.64291e-16 2.67493e-12 ENSG00000142798 3339 HSPG2
12 7.62729e-16 2.71169e-12 ENSG00000196136 12 SERPINA3
3691 1.09383e-15 3.11108e-12 ENSG00000132470 3691 ITGB4
... ... ... ... ... ...
10745 0.0168541 0.0496926 ENSG00000116793 10745 PHTF1
8495 0.0168566 0.0496926 ENSG00000166387 8495 PPFIBP2
54039 0.0169148 0.0498541 ENSG00000183570 54039 PCBP3
57592 0.0169391 0.0499154 ENSG00000143373 57592 ZNF687
8708 0.0169521 0.0499431 ENSG00000172318 8708 B3GALT1
GENENAME CHR UNIPROT ALIAS
<character> <character> <character> <character>
64005 myosin IG 7 B0I1T2 HA2
7052 transglutaminase 2 20 P21980 G(h)
3339 heparan sulfate prot.. 1 P98160 HSPG
12 serpin family A memb.. 14 A0A024R6P0 AACT
3691 integrin subunit bet.. 17 A0A024R8T0 CD104
... ... ... ... ...
10745 putative homeodomain.. 1 Q9UMS5 PHTF
8495 PPFIA binding protei.. 11 Q8ND30 Cclp1
54039 poly(rC) binding pro.. 21 P57721 ALPHA-CP3
57592 zinc finger protein .. 1 Q8N1G0 PDB6
8708 beta-1,3-galactosylt.. 2 Q9Y5Z6 beta3Gal-T1
-------
seqinfo: 595 sequences (1 circular) from hg38 genomesigRegions <- keepSeqlevels(sigRegions, value = c("chr1","chr2","chr3","chr4","chr5","chr6","chr7",
"chr8","chr9","chr10","chr11","chr12","chr13","chr14",
"chr15","chr16","chr17","chr18","chr19","chr20",
"chr21","chr22","chrX","chrY"),pruning.mode="tidy")
#seqlevels(sigRegions)
Score <- -log10(sigRegions$FDR)
rbPal <-colorRampPalette(c("blue", "red"))
logfc <- pmax(sigRegions$logFC, -5)
logfc <- pmin(logfc , 5)
Col <- rbPal(10)[as.numeric(cut(logfc, breaks = 10))]
mcols(sigRegions)$score <- Score
mcols(sigRegions)$itemRgb <- Col
#export(sigRegions , con = "topHitshuman_hippo.bed")
top200 <- sigRegions[order(sigRegions$LR,decreasing = TRUE)]
plotGrandLinear(top200 , aes(y = logFC))using coord:genome to parse x scale
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
mcols(top200)$UpRegulated <- mcols(top200)$logFC > 0
mcols(top200)$DownRegulated <- mcols(top200)$logFC < 0
autoplot(top200,layout="karyogram",aes(color=DownRegulated,fill=DownRegulated))Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
results_hippo_RNA<-as_tibble(results_rna_annotated_hth_filt_coding2)
row.names(results_hippo_RNA)<-results_hippo_RNA$ENSEMBL
saveRDS(results_hippo_RNA, file = "results_hippo_RNA.rds")
saveRDS(sigGenes,file="sigGenes_hippo.rds")Results from differential expression analysis for hippocampus samples
results_rna_annotated_hth_filt_coding2.dt <- DT::datatable(results_rna_annotated_hth_filt_coding2, rownames=TRUE, class="white-space: nowrap", escape=FALSE)
results_rna_annotated_hth_filt_coding2.dt
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] DT_0.25.1
[2] kableExtra_1.3.4
[3] circlize_0.4.15
[4] ComplexHeatmap_2.8.0
[5] pathview_1.32.0
[6] forcats_0.5.2
[7] stringr_1.4.1
[8] purrr_0.3.4
[9] readr_2.1.2
[10] tidyr_1.2.1
[11] tibble_3.1.8
[12] tidyverse_1.3.2
[13] karyoploteR_1.18.0
[14] regioneR_1.24.0
[15] ggridges_0.5.3
[16] enrichplot_1.12.3
[17] clusterProfiler_4.0.5
[18] ggbio_1.40.0
[19] rtracklayer_1.52.1
[20] TxDb.Hsapiens.UCSC.hg38.knownGene_3.13.0
[21] GenomicFeatures_1.44.2
[22] GenomicRanges_1.44.0
[23] GenomeInfoDb_1.28.4
[24] pcaExplorer_2.18.0
[25] org.Hs.eg.db_3.13.0
[26] AnnotationDbi_1.54.1
[27] IRanges_2.28.0
[28] S4Vectors_0.32.3
[29] Biobase_2.54.0
[30] BiocGenerics_0.40.0
[31] DBI_1.1.3
[32] ggrepel_0.9.1
[33] openxlsx_4.2.5
[34] edgeR_3.34.1
[35] limma_3.50.1
[36] dplyr_1.0.10
[37] ggplot2_3.3.6
[38] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 svglite_2.1.0
[3] Hmisc_4.7-1 ps_1.7.1
[5] Rsamtools_2.8.0 foreach_1.5.2
[7] rprojroot_2.0.3 crayon_1.5.1
[9] MASS_7.3-58.1 nlme_3.1-159
[11] backports_1.4.1 reprex_2.0.2
[13] GOSemSim_2.18.1 rlang_1.0.5
[15] XVector_0.32.0 readxl_1.4.1
[17] irlba_2.3.5 SparseM_1.81
[19] callr_3.7.2 filelock_1.0.2
[21] GOstats_2.58.0 BiocParallel_1.28.3
[23] rjson_0.2.21 bit64_4.0.5
[25] glue_1.6.2 pheatmap_1.0.12
[27] rngtools_1.5.2 parallel_4.1.0
[29] processx_3.7.0 shinyAce_0.4.2
[31] shinydashboard_0.7.2 DOSE_3.18.3
[33] haven_2.5.1 tidyselect_1.1.2
[35] SummarizedExperiment_1.22.0 XML_3.99-0.10
[37] GenomicAlignments_1.28.0 xtable_1.8-4
[39] magrittr_2.0.3 evaluate_0.16
[41] cli_3.4.0 zlibbioc_1.40.0
[43] rstudioapi_0.14 whisker_0.4
[45] bslib_0.4.0 rpart_4.1.16
[47] fastmatch_1.1-3 ensembldb_2.16.4
[49] treeio_1.16.2 shiny_1.7.2
[51] BiocSingular_1.8.1 xfun_0.33
[53] clue_0.3-61 cluster_2.1.4
[55] tidygraph_1.2.2 TSP_1.2-1
[57] KEGGREST_1.32.0 biovizBase_1.40.0
[59] threejs_0.3.3 ape_5.6-2
[61] dendextend_1.16.0 Biostrings_2.60.2
[63] png_0.1-7 reshape_0.8.9
[65] withr_2.5.0 shinyBS_0.61.1
[67] bitops_1.0-7 ggforce_0.3.4
[69] RBGL_1.68.0 plyr_1.8.7
[71] cellranger_1.1.0 GSEABase_1.54.0
[73] AnnotationFilter_1.16.0 dqrng_0.3.0
[75] pillar_1.8.1 GlobalOptions_0.1.2
[77] cachem_1.0.6 fs_1.5.2
[79] GetoptLong_1.0.5 DelayedMatrixStats_1.14.3
[81] vctrs_0.4.1 ellipsis_0.3.2
[83] generics_0.1.3 NMF_0.24.0
[85] tools_4.1.0 foreign_0.8-82
[87] munsell_0.5.0 tweenr_2.0.2
[89] fgsea_1.18.0 DelayedArray_0.18.0
[91] fastmap_1.1.0 compiler_4.1.0
[93] httpuv_1.6.6 pkgmaker_0.32.2
[95] plotly_4.10.0 GenomeInfoDbData_1.2.6
[97] gridExtra_2.3 lattice_0.20-45
[99] deldir_1.0-6 AnnotationForge_1.34.1
[101] utf8_1.2.2 later_1.3.0
[103] BiocFileCache_2.0.0 jsonlite_1.8.0
[105] GGally_2.1.2 scales_1.2.1
[107] ScaledMatrix_1.0.0 graph_1.70.0
[109] sparseMatrixStats_1.4.2 tidytree_0.4.0
[111] genefilter_1.74.1 lazyeval_0.2.2
[113] promises_1.2.0.1 doParallel_1.0.17
[115] latticeExtra_0.6-30 checkmate_2.1.0
[117] rmarkdown_2.16 cowplot_1.1.1
[119] webshot_0.5.3 dichromat_2.0-0.1
[121] downloader_0.4 BSgenome_1.60.0
[123] igraph_1.3.4 survival_3.4-0
[125] yaml_2.3.5 systemfonts_1.0.4
[127] htmltools_0.5.3 memoise_2.0.1
[129] VariantAnnotation_1.38.0 BiocIO_1.2.0
[131] locfit_1.5-9.6 seriation_1.3.6
[133] PCAtools_2.4.0 graphlayouts_0.8.1
[135] viridisLite_0.4.1 digest_0.6.29
[137] assertthat_0.2.1 mime_0.12
[139] rappdirs_0.3.3 registry_0.5-1
[141] RSQLite_2.2.17 yulab.utils_0.0.5
[143] data.table_1.14.2 blob_1.2.3
[145] labeling_0.4.2 splines_4.1.0
[147] Formula_1.2-4 Cairo_1.6-0
[149] googledrive_2.0.0 OrganismDbi_1.34.0
[151] ProtGenerics_1.26.0 RCurl_1.98-1.8
[153] broom_1.0.1 hms_1.1.2
[155] modelr_0.1.9 colorspace_2.0-3
[157] base64enc_0.1-3 BiocManager_1.30.18
[159] shape_1.4.6 aplot_0.1.7
[161] nnet_7.3-17 sass_0.4.2
[163] Rcpp_1.0.9 fansi_1.0.3
[165] tzdb_0.3.0 R6_2.5.1
[167] lifecycle_1.0.2 zip_2.2.1
[169] curl_4.3.2 googlesheets4_1.0.1
[171] jquerylib_0.1.4 DO.db_2.9
[173] Matrix_1.4-1 qvalue_2.24.0
[175] RColorBrewer_1.1-3 iterators_1.0.14
[177] topGO_2.44.0 htmlwidgets_1.5.4
[179] bamsignals_1.24.0 beachmat_2.8.1
[181] polyclip_1.10-0 biomaRt_2.48.3
[183] crosstalk_1.2.0 shadowtext_0.1.2
[185] gridGraphics_0.5-1 rvest_1.0.3
[187] htmlTable_2.4.1 patchwork_1.1.2
[189] KEGGgraph_1.52.0 codetools_0.2-18
[191] matrixStats_0.62.0 lubridate_1.8.0
[193] GO.db_3.13.0 getPass_0.2-2
[195] prettyunits_1.1.1 dbplyr_2.2.1
[197] gridBase_0.4-7 gtable_0.3.1
[199] git2r_0.30.1 highr_0.9
[201] ggfun_0.0.7 httr_1.4.4
[203] stringi_1.7.8 progress_1.2.2
[205] reshape2_1.4.4 farver_2.1.1
[207] heatmaply_1.3.0 annotate_1.70.0
[209] viridis_0.6.2 Rgraphviz_2.36.0
[211] ggtree_3.0.4 xml2_1.3.3
[213] bezier_1.1.2 restfulr_0.0.15
[215] interp_1.1-3 geneplotter_1.70.0
[217] ggplotify_0.1.0 Category_2.58.0
[219] DESeq2_1.32.0 bit_4.0.4
[221] scatterpie_0.1.8 jpeg_0.1-9
[223] MatrixGenerics_1.4.3 ggraph_2.0.6
[225] pkgconfig_2.0.3 gargle_1.2.1
[227] knitr_1.40